Structural Representation

Structural representation is the mode of standing-for in which a representation preserves the relational structure of its target rather than copying its surface properties. A subway map represents the rail network not by looking like tracks but by preserving connectivity, sequence, and relative distance. This distinguishes structural representation from iconic representation (resemblance-based) and symbolic representation (arbitrary convention-based) — though in practice these modes often blend.

The concept is central to cognitive science and artificial intelligence because it explains how both biological brains and artificial systems can represent complex domains without needing detailed copies. A connectionist network's distributed activation pattern may structurally correspond to the similarity structure of its input domain — preserving which items are like which others — without representing any item iconically. The same logic applies to mental models, scale models, and scientific theories: what makes them representational is not what they look like but what relations they preserve.

The insistence on resemblance as the mark of representation is a legacy of pre-scientific intuition. The most powerful representations in science and cognition are structural, not iconic — and the failure to recognize this has led philosophy of mind to chase the wrong properties for decades.